Self-Assembled Devices May Transform Manufacturing

Chain-type robots such as CKbot look like snakes or trees, and are made of a series of connected modules. “In lattice-based robots, modules are arranged in a regular pattern; common architectures include square or hexagonal 2D shapes and cubes or dodecahedrons,” Pickem told us. A robot either rearranges or adds modules to form a shape, the self-assembly approach, or removes modules it doesn’t need, the self-disassembly approach. Hybrids such as SuperBot, created to aid NASA in planetary exploration, combine chain-type with lattice-based implementations. These assemble modules to form linear shapes or fold up to form solid shapes.

Pickem says he’s only seen prototypes so far, not finished products, of these three types. On the software side of modular robot development, several algorithms have been proven to produce a specific reconfiguration sequence. The focus has shifted recently from simulation to building hardware prototypes. The three main hardware challenges are actuation, connectors, and structural stability.

The 3D brick approach to self-assembly at the nanoscale is based on short synthetic strands of DNA that form building blocks, which self-assemble into 100 different, precise 3D shapes such as letters and numbers. Like the models of 80 of these shapes shown here, each unique shape measures about 25 nm per side.

“Actuation challenges include the fact that robots must be small, yet strong enough to lift themselves and other modules,” Pickem said. “Connectors must form reliable and strong connections for structural stability, yet also break when necessary. Because modular robots don’t have the same structural stability as monolithic robots designed for the manufacturing floor, the challenge there is how to make them both light and small, as well as strong.”

Self-reconfigurable robots can be built using different design principles: deterministic or stochastic, self-assembling or self-disassembling, centralized or decentralized, and homogeneous or heterogeneous. Deterministic schemes can locate modules at any given time, but require more planning and control because they tell every module what to do. In stochastic architectures, modules’ connections and disconnections happen randomly, and are more likely to occur as module count increases.

Self-assembly schemes are more common than self-disassembly schemes, said Pickem. One self-disassembly method has been built by a team led by Daniela Rus, a principal investigator at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL). Developed under the aegis of CSAIL’s Distributed Robotics Laboratory (DRL), small robotic cubes self-disassemble to duplicate an object placed in a heap of them. Measuring 12 mm per side, the Smart Pebble robotic cubes communicate how to align themselves to duplicate an object’s shape using distributed algorithms. First, they form a grid using electropermanent magnets, then they discard unneeded cubes. The team wants to scale cube size down to 1 mm per side to build thousands of cubes on a silicon wafer using lithography.

In a centralized architecture one agent plans for all modules, but in a decentralized scheme every module plans for itself based on information it observes or gathers by communicating with neighbors. Generally, large numbers of robots can be controlled more efficiently with decentralized control schemes.

In a homogeneous architecture, modules have the same properties, so are interchangeable and can be replaced easily, a more robust design than specialized, fixed-architecture monolithic robots. Robots made of heterogeneous modules can be more flexible in their capabilities. “One can think of a mobile robot with dedicated battery, wheel, actuation, or sensor modules, which has all the capabilities of its individual modules,” said Pickem. “Overall functionality is improved at a much lower cost. To extend a homogeneous robot, all modules must be extended with the desired capability.”

Because all of this work is still in R&D it might be easy to dismiss it as blue-sky. But I discovered while doing the background research for this article that many of these projects have been underway for several years, and much of what's being done now is second- or even third-generation R&D. There's an awful lot of brains and money aimed at developing self-assembling. self-reconfiguring robots. I came away with the feeling that the future is going to be very different, indeed.

In the past, there has been the myth that robots create more jobs (in robot design and manufacturing systems design) than they replace. But it's simple economics -- if robots create more jobs than they replace they would not be economically feasible -- and apparently they are economically feasible.

Rob, I agree--in fact, it's simple arithmetic. I'm getting a little tired of hearing about all the supposed new jobs that will be created instead of all the jobs that will, obviously, in fact be taken away. What's also ignored in those arguments is--what happens to all the people whose jobs are taken away? And what happens to all the people trained for, and dependent on, that shrinking pool of good blue collar jobs?

For this approach to gain traction, there may be a need for a killer app or specific market for these modular, self-reconfiguring robots to prove themselves in. OEM machines are often very niche oriented (relatively low number of new machines per year and a huge installed base developed over a much longer period). Makes it difficult for new approaches to break in.

In the early years of computers, the computers did indeed create more jobs than they eliminated. That was partly due to poor implementation and apps that were not well designed for labor savings. That, of course, changed in time.

With robots, I wouldn't expect that delay. I would imagine the apps are available as the robots are created. So the labor savings would be immediate.

Ann, this technology seems to reflect what we've already seen in numerous sci-fi books and movies. There are many examples, but one that jumps to mind is Terminator 2, where the terminator robot re-assembles itself after getting shot.

@Rob: The problem with your "simple economics" argument ("if robots create more jobs than they replace they would not be economically feasible") is that economics is not a zero-sum game. Higher productivity creates economic growth, which creates jobs.

Companies don't make money by eliminating jobs, they make money by selling products. If automation allows a company to make products at a lower cost, they can sell more products. If they sell more products, they will make more money. If the company makes more money, they will have more money to invest -- including in new employees.

Al, the main apps I've heard of mentioned more than once are consumer, like reconfigurable furniture, or reconfigurable robots for space exploration and search and rescue. That's the macro-level tehcnmologies. For the nano and micro-Ievel it's usually various medical uses such as drug delivery mechanisms.

Instead of sifting through huge amounts of technical data looking for answers to assembly problems, engineers can now benefit from 3M's new initiative -- 3M Assembly Solutions. The company has organized its wealth of adhesive and tape solutions into six typical application areas, making it easier to find the best products to solve their real-world assembly and bonding problems.

Many of the materials in this slideshow are resins or elastomers, plus reinforced materials, styrenics, and PLA masterbatches. Applications range from automotive and aerospace to industrial, consumer electronics and wearables, consumer goods, medical and healthcare, as well as sporting goods, and materials for protecting food and beverages.

Engineers trying to keep track of the ever-ballooning number of materials and machines for additive manufacturing and 3D printing now have some relief: a free searchable database with more than 350 machines and 450 different materials.

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